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Automating Enterprise Business Processes using AI
Naghi PrasadXu Miao
22
AI-driven enterprise applications
•Business processes mapped to an AI engine to enable business efficiencies.
•4 business processes being automated by AI Customer Support
Recruiting
Content Marketing
AdTech
• We will conclude with lessons learned from being very involved in these companies since inception.
33
But then what is AI? – Lessons Learned
•AI is a rich source of interesting toolsLot more than Deep Learning, CNN, Generative Adversarial
Networks!!
Suite of techniques to evoke intelligence : Categorizers, Regression, NLP, Case-Based reasoning etc.
•Domain driven rather than technique drivenLet the domain drive the problem solving and which techniques
you use from the bag
•Interesting Data strategies
•AI application is like a raisin bread : it is still 90% bread
4
NevaAI-Driven Automation for Service and Support
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Why Neva?
Customer service organizations must improve support quality while reducing delivery costs.
Key challenges
Fragmented knowledge from disparate knowledge sources and
enterprise systems, and decentralized change management.
Inefficient decision-making due to gap between front and back
office, frequent changes, and inability to continuously train human
agents.
Fractured user experience due to omni-channel, modern support
outside work and inefficient, human-based support at work
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Structured
Complicated Data Environment
Structured
Operational DBs
Datawarehouse
APsEnterprise APIs
StructuredUnstructured
Knowledge Articles
Forum Posts
Screen shots
StructuredSemi-structured
E-mails
Log/message history
8
Model Driven Intelligent Data Process
Search GraphMySQL RedisHive/Presto
Active Learning
Input Query analysis
Knowledge
Releva
nce
Indexing
Ranking
BusinessLogic
Document understanding
output
Inference Learning
SQL
OLTP OLAP
SkitLearn
www.swooptalent.com
Talent Data Cloud with SwoopTalent
www.swooptalent.com
Your PRIVATE data backbone
Data from ALL sources matched & made available
Private Talent Data Cloud
Production ATS - cloud
Data from prior ATS
CRM and other live systems -
cloud
Hundreds of millions of social talent records gathered by Swoop
Resumes, Spreadsheets,
etc
www.swooptalent.com
Candidate Profiles on SwoopTalent
External (public) records
Combined data: rich, fresh, searchable, analyzableInternal ATS records
www.swooptalent.com
Swoop AI Layer
More Structured Less Structured
ATS, CRMXML, Excel, Flat
Files,Social Media, Niche
Forums, Society BoardsDocs, PDF, JPG,
Supervised Learning
TokenizationPart of Speech
Named Entity Recognition
Custom Pipelines
Unsupervised Learning
Clustering Similarity
Latent Semantic Analysis, SVD, Word2Vec
Topic Modeling (160 Million Profiles)
Data Data
Data Data
Search
Semantic Query
Processing
Topic Modeling
Application
Up IQAutonomous MarketingTM
Copyright 2016 Confidential & Proprietary | Not Meant for Distribution
Automatically generates statistically relevant marketing content that is highly personalized
10x better conversion rates for organic search
Enterprises Journey to Autonomous Marketing
Data
Sync
Banks Data
Social Data
Public Records
Data
Cleansing
Data is Engineered
Content
Creation
Search Content
Social Content
Email & Text
Machine
Learning
NLPK
Data Science
• Markovian Modeling
Up IQ to Power Banks: SEM Campaigns, & Landing Pages
Customers Journey, from Discovery to Acquisition
Personalized
Banks
Retail Banks
Mortgage Banks
Online Lenders
Relevant
Bank Staff
Ranked Bank Staff
Content
Discovery
Search Content
Social Content
Email & Text
• Information Theoretic Scoring• Sentiment Analysis
Deep Forest Mediaa Rakuten Company
Cross Device Graph
1919
Machine learning models device graph relationships : naive Bayes modeling &heuristics for pruning.
172.0.0.217Feature engineering (UID, IP, user agent, referral url, login email etc.)
Data Collection (cookie-sync, exchanges, ad impression, native sdk, 3rd party data
Identify users across smartphones, tablets & desktops
Bid Price Optimization
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• A dynamic pricing algorithm
– maximizes the expected value of gain after winning an auction, or 𝑏 =𝑎𝑟𝑔𝑚𝑎𝑥𝑏 𝐸 𝑔𝑎𝑖𝑛
– adjusts automatically to meet business requirements (ex. CPM margin) using a feedback loop
auction data
user data
win rate
win price
purchase prediction
ctr
bidding strategy
bid price
business requirements
alpha
• Machine learning modelswin rate – binary classification (Random forest)win price – regressionpurchase prediction – binary classification
(Random forest)CTR – binary classification
2121
But then what is AI? – Lessons Learned
•AI is a rich source of toolsDeep Learning, CNN, Generative Adversarial Networks
Categorizers, Regression, NLP, Case-Based reasoning etc.
•Domain driven rather than technique driven
•Interesting Data strategies
•AI application is like a raisin bread : it is still 90% bread
Questions?
Naghi Prasad Xu Miao
Neva.ai
Neva: Xu Miao [email protected], Naghi Prasad [email protected] : Maksym Bychkov, [email protected] : Satish Sallakonda [email protected] : Baiji He, [email protected]
